Update README.md

README.md

@@ -1,47 +1,20 @@
 
 title: Satellite Classification Dashboardemoji: 🛰️colorFrom: bluecolorTo: purplesdk: gradiosdk_version: 5.0.2app_file: app.pypinned: false
 🛰️ Satellite Classification Dashboard
-A streamlined deep learning application for classifying satellite images using state-of-the-art neural network models. This interactive Gradio-based dashboard allows users to upload satellite images, select multiple models for classification, and view predictions with confidence scores and visualizations, all hosted on Hugging Face Spaces.
-🌟 Features
+A Gradio-based application for classifying satellite images using pre-trained deep learning models. Upload a PNG, JPG, or JPEG image, select one or more models (Custom CNN, MobileNetV2, EfficientNetB0, DenseNet121), and view predictions with confidence scores and visualizations.
+Quick Start
 
-Interactive Interface: Built with Gradio for a simple, user-friendly web experience.
-Real-Time Image Classification: Upload PNG, JPG, or JPEG images to classify satellites and space debris.
-Multiple Model Support: Choose from four pre-trained models with varying strengths:
-Custom CNN: Tailored for satellite imagery (95.2% accuracy).
-MobileNetV2: Lightweight and fast (92.8% accuracy, 18ms inference).
-EfficientNetB0: Best accuracy-efficiency balance (96.4% accuracy).
-DenseNet121: Complex pattern recognition (94.7% accuracy).
-
-
-Visualizations: Interactive Plotly charts for confidence comparison and class probability distribution.
-Model Recommendation: Automatically suggests the best model based on confidence and performance metrics.
-Supported Classes: Classifies 11 categories:
-AcrimSat, Aquarius, Aura, Calipso, Cloudsat, CubeSat, Debris, Jason, Sentinel-6, TRMM, Terra
-
-
-
-🎯 Quick Start
-Try the Live Demo
-Visit the Hugging Face Space to use the application directly in your browser: https://huggingface.co/spaces/your-username/Satellite-Classification-Gradio
-Local Installation
-
-Clone the repository:git clone https://huggingface.co/spaces/your-username/Satellite-Classification-Gradio
+Try the Live Demo: Visit https://huggingface.co/spaces/your-username/Satellite-Classification-Gradio.
+Local Setup:git clone https://huggingface.co/spaces/your-username/Satellite-Classification-Gradio
 cd Satellite-Classification-Gradio
-
-
-Create a virtual environment (optional but recommended):python -m venv venv
+python -m venv venv
 source venv/bin/activate  # On Windows: venv\Scripts\activate
+pip install -r requirements.txt
+python app.py
 
+Open http://localhost:7860 in your browser.
 
-Install dependencies:pip install -r requirements.txt
-
-
-Run the application:python app.py
-
-
-Open your browser and navigate to http://localhost:7860.
-
-📦 Dependencies
+Dependencies
 Listed in requirements.txt:
 
 gradio==5.0.2
@@ -53,195 +26,35 @@ Pillow==10.4.0
 requests==2.32.3
 protobuf==3.20.3
 
-🎮 How to Use
-
-Upload Image: Upload a satellite image (PNG, JPG, or JPEG).
-Select Models: Choose one or more models (Custom CNN, MobileNetV2, EfficientNetB0, DenseNet121).
-Classify: Click "Classify Image" to get predictions.
-View Results:
-Table of predictions (model, predicted class, confidence, inference time).
-Recommended model based on confidence and performance.
-Confidence comparison bar chart.
-Top 5 class probabilities for the recommended model.
-
-
-
-📊 Model Performance
-
-
-
-Model
-Accuracy
-Precision
-Recall
-F1-Score
-Inference Time
-Model Size
-
-
-
-EfficientNetB0
-96.4%
-96.1%
-96.2%
-96.1%
-35ms
-20.1MB
-
-
-Custom CNN
-95.2%
-94.8%
-95.1%
-94.9%
-45ms
-25.3MB
-
-
-DenseNet121
-94.7%
-94.2%
-94.5%
-94.3%
-52ms
-32.8MB
-
-
-MobileNetV2
-92.8%
-92.1%
-92.5%
-92.3%
-18ms
-8.7MB
-
-
-🎯 Model Selection Guide
-
-Best Overall Accuracy: EfficientNetB0 (96.4%)
-Fastest Inference: MobileNetV2 (18ms)
-Most Lightweight: MobileNetV2 (8.7MB)
-Best Balance: EfficientNetB0 (High accuracy + efficiency)
-Mobile/Edge Deployment: MobileNetV2
-Research/High Accuracy: EfficientNetB0 or DenseNet121
-
-🏗️ Architecture
-Model Sources
-All models are hosted on Hugging Face Model Hub:
-
-Custom CNN: Bhavi23/Custom_CNN
-MobileNetV2: Bhavi23/MobilenetV2
-EfficientNetB0: Bhavi23/EfficientNet_B0
-DenseNet121: Bhavi23/DenseNet
-
-Data Pipeline
-
-Image Upload: Supports PNG, JPG, JPEG formats.
-Preprocessing: Resize to 224x224, normalize to [0,1].
-Model Inference: Multi-model prediction with timing.
-Post-processing: Confidence scoring and model recommendations.
-
-🔧 Technical Details
-
-Input Requirements:
-Image Format: PNG, JPG, JPEG
-Input Size: 224x224x3 (RGB)
-Preprocessing: Automatic resizing and normalization
-
-
-Output Format:
-Class Prediction: One of 11 satellite categories
-Confidence Score: Percentage confidence (0-100%)
-Inference Time: Milliseconds for prediction
-Probability Distribution: Full softmax output for all classes
-
-
-Performance Optimization:
-Model Caching: Models loaded on-demand.
-Efficient Preprocessing: Optimized image pipeline.
-Memory Management: Automatic cleanup of model objects.
-
-
-
-🚢 Deployment
-The application is deployed on Hugging Face Spaces using:
-
-Runtime: Python 3.9
-Framework: Gradio
-Resources: CPU-optimized for inference
-
-Docker Deployment (Optional)
-If needed, use this Dockerfile:
-FROM python:3.9-slim
-WORKDIR /app
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-COPY . .
-EXPOSE 7860
-CMD ["python", "app.py"]
-
-🤝 Contributing
-We welcome contributions! Please follow these steps:
-
-Fork the repository.
-Create a feature branch (git checkout -b feature/amazing-feature).
-Commit changes (git commit -m 'Add amazing feature').
-Push to branch (git push origin feature/amazing-feature).
-Open a Pull Request.
-
-Development Setup
-# Clone your fork
-git clone https://huggingface.co/spaces/your-username/Satellite-Classification-Gradio
-# Create virtual environment
-python -m venv venv
-source venv/bin/activate  # On Windows: venv\Scripts\activate
-# Install dependencies
-pip install -r requirements.txt
-# Run in development mode
-python app.py
-
-📄 License
-This project is licensed under the MIT License - see the LICENSE file for details.
-🙏 Acknowledgments
+Troubleshooting
+Missing configuration in README
 
-Dataset: Spark 2021 dataset used for training.
-Frameworks: TensorFlow, Gradio, Plotly.
-Models: Pre-trained architectures from TensorFlow/Keras.
-Hosting: Hugging Face Spaces for deployment.
+Cause: The README.md lacks the required YAML front matter.
+Fix:
+Ensure this README.md is saved in the repository root with exact filename README.md.
+Verify YAML syntax (no extra spaces, correct indentation).
+Push to repository:git add README.md
+git commit -m "Add YAML front matter to README.md"
+git push
 
-📞 Support
 
-Issues: GitHub Issues
-Discussions: Hugging Face Discussions
-Email: bhavithrass@gmail.com
+Restart the Space in the Settings tab.
 
-🔮 Future Enhancements
 
-Real-time video classification.
-Batch processing for multiple images.
-API endpoint for programmatic access.
-Advanced visualizations with satellite orbit data.
 
-🛠️ Troubleshooting
-If you encounter errors during deployment or runtime, try the following:
 ModuleNotFoundError: No module named 'tensorflow'
 
-Cause: TensorFlow failed to install during the Space build.
+Cause: TensorFlow failed to install during the build.
 Fix:
-Ensure requirements.txt includes tensorflow-cpu==2.15.0 and protobuf==3.20.3.
-Verify the file is in the repository root:git add requirements.txt
-git commit -m "Update requirements.txt"
-git push
-
-
-Check build logs in the Space’s Settings tab for dependency installation errors.
-Test locally to confirm compatibility:python -m venv venv
+Verify requirements.txt includes tensorflow-cpu==2.15.0 and protobuf==3.20.3.
+Check build logs in the Space’s Settings tab.
+Test locally:python -m venv venv
 source venv/bin/activate
 pip install -r requirements.txt
 python app.py
 
 
-If the issue persists, use a Dockerfile to enforce Python 3.9:FROM python:3.9-slim
+If unresolved, add a Dockerfile:FROM python:3.9-slim
 WORKDIR /app
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
@@ -250,27 +63,11 @@ EXPOSE 7860
 CMD ["python", "app.py"]
 
 
-Restart the Space after pushing changes.
-
-
-Contact Support: If unresolved, open an issue on Hugging Face or check the Hugging Face Forums.
-
-Missing configuration in README
-
-Cause: The README.md lacked the required YAML front matter.
-Fix: This file includes the correct YAML header (see top). Ensure it is saved as README.md in the repository root:git add README.md
-git commit -m "Add YAML front matter to README.md"
-git push
+Switch the Space’s SDK to Docker and push the Dockerfile.
 
 
-Restart Space: Go to the Space’s Settings tab and click Restart Space to apply changes.
 
-📊 Usage Statistics
-Track your application usage:
+Support
 
-Classifications Performed: Real-time counter.
-Popular Models: Usage analytics.
-Performance Metrics: Response time tracking.
-
-Built with ❤️ using Gradio and TensorFlow
-For more information, visit our Hugging Face Space
+Issues: Hugging Face Discussions
+Email: bhavithrass@gmail.com